In the field of medical imaging, image segmentation is the process of partitioning a digital image into multiple segments of image elements being pixels or voxels. For the purpose of image based medical analysis, medical treatment planning or for the improvement of the visualization or context based navigation in medical images image segmentation is an indispensable technology. For instance, for radiation therapy treatment planning image segmentation is important to prevent radiosensitive anatomical structures from damage.
There exist different ways of segmenting images. For instance, threshold or appearance based segmentation algorithms, segmentation algorithms based on atlas registration or shape model based segmentation algorithms.
Among all the image segmentation methods, interactive image segmentation techniques are required if automatic methods are not available for certain segmentation tasks or if results of automatic segmentation algorithms need manual corrections. For instance, one accepted technique for interactive image segmentation is manual contouring of image slices. In contrast to automatic image segmentation, which is usually only possible for certain anatomical structures, interactive image segmentation provides higher user flexibility. For instance, it can be utilized in conjunction with different imaging modalities and/or specific imaging protocols.
US 2010/022489 A1 discloses a computer-implemented method of segmenting images comprising the steps of receiving an image, generating a segment of the image based on initial segmentation parameters, classifying the features of the generated segment and generating at least one revised segmentation parameter. Further, a contour of interest can be extracted and a different contour can be produced when the segmentation parameters are varied.
Vincent, Martin et al., “A Cognitive Vision Approach to Image Segmentation”, “Tools in Artificial Intelligence”, 208, InTech Education and Publishing, pp. 265-294, discloses a framework for manual segmentations of training images with closed outlined regions. In particular, the framework includes defining a segmentation quality function and optimizing a parameter set by minimizing or maximizing the segmentation quality function.
EP 1 526 481 A2 discloses a method and an apparatus for segmenting a first region and a second region, wherein the method includes determining using a learning machine, based on one or more of the color arrangements, which pixels of the image satisfy criteria for classification as associated with the first region and which pixels of the image satisfy criteria for classification as associated with the second region.
William B. Kerr et al., “A Methodology and Metric for Quantitative Analysis and Parameter Optimization of Unsupervised, Multi-Region Image Segmentation”, Proceeding of the 8th IASTED International Conference on Signal and Image Processing, 14 Aug. 2006, pp. 243-248, discloses the use of a methodology for quantitative analysis and parameter optimization of unsupervised, multi-region image segmentation, wherein the methodology is based on eight individual performance measures. A metric based on a statistical analysis of the overlap between machine segmented and corresponding ground truth images to evaluate and optimize algorithm parameters is disclosed, wherein inter-algorithm performance for unsupervised segmentation algorithms is compared.
Interactive image segmentation tools known in the art are, however, limited in their efficiency, since a large number of technical parameters often need to be selected and optimally adjusted. Moreover, the reliability of the segmentation results is often limited as well, for instance due to an inaccurate contour correction. This results in high costs due to further image segmentation as well as non-optimal radiation therapy treatment plans having fatal consequences.